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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Energy efficient distributed intelligence on cognitive IoT gateway using MQTT protocols Krishna, Anitha; Balasubramanian, Muthu Kumar; Srinivas Desikachar, Venkatesh Prasad Kadaba
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp2059-2069

Abstract

Internet of Things (IoT) facilitates communication between machines and devices which plays a crucial role in the conservation of energy. In largescale multidomain environments securing the data exchange among various IoT devices and key sharing creates a significant challenge. However, the message queuing telemetry transport (MQTT) lacks functional security mechanisms as well as mutual authentication between brokers and clients. To address these issues, a novel Cognitive IoT in Teroperability Recognition USing deep learning (CITRUS) framework is developed for real-time decision-making and sharing information among multiple IoT systems. Initially, the healthcare and weather data are collected remotely by using interoperable sensors which are then fed to the deep learning (DL) module for efficient decision-making. The MQTT module makes an energy-efficient IoT data communication over a resource-constrained network and the QoS1 introduces an acknowledgment and retransmission mechanism to ensure message delivery. The efficacy of the CITRUS model has been analyzed in terms of accuracy (AC), recall (RC), F1-score (F1S), sensitivity, packet delivery ratio (PDR), transmission speed, communication overhead, packet loss ratio (PLR) and delay. The experimental result shows that the CITRUS method achieves 89.89% of delay whereas, the IHPEC, SemBox, and DynoIoT methods achieve 161.63%, 128.99%, and 111.70% respectively for efficient data transmission.
Core methodological classes of text extraction and localization-a snapshot of approaches Jayaram, Dayananda Kodala; Devegowda, Puttegowda
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp455-465

Abstract

The motivation to work on text extraction and localization is quite a substantial that potentially influences a larger area of application right from business intelligence to advanced data analytics. At present, there are massive archives of literatures addressing varying ranges of problems associated with text extraction and localization with an effective realization of respective contribution as well as on-going issues. However, problem statement is that all these massive implementation studies are further required to converge down in order to realize the core classes of methodologies involved in text extraction. Hence, this manuscript uses desk research methodology to address this issue by presenting a compact insight of core methodological classes where all the recent implementation work are converged down to understand its strength and weakness. The research outcome contributes towards facilitating information of current research trend and identified research gap. The proposed review study assists in undertaking decision of suitable approach of text extraction, localization, detection, recognition, and classification.
Investigating the recall efficiency in abstractive summarization: an experimental based comparative study Anuradha, Surabhi; Sheshikala, Martha
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp446-454

Abstract

This study explores text summarization, a critical component of natural language processing (NLP), specifically targeting scientific documents. Traditional extractive summarization, which relies on the original wording, often results in disjointed sequences of sentences and fails to convey key ideas concisely. To address these issues and ensure comprehensive inclusion of relevant details, our research aims to improve the coherence and completeness of summaries. We employed 25 different large language models (LLMs) to evaluate their performance in generating abstractive summaries of scholarly scientific documents. A recall-oriented evaluation of the generated summaries revealed that LLMs such as 'Claude v2.1,' 'PPLX 70B Online,' and 'Mistral 7B Instruct' demonstrated exceptional performance with ROUGE-1 scores of 0.92, 0.88, and 0.85, respectively, supported by high precision and recall values from bidirectional encoder representations from transformers (BERT) scores (0.902, 0.894, and 0.888). These findings offer valuable insights for NLP researchers, laying the foundation for future advancements in LLMs for summarization. The study highlights potential improvements in text summarization techniques, benefiting various NLP applications.
Predictive modelling of osteoporosis and effect of BMI on the risk of fracture in femur bone using COMSOL Multiphysics: a computational modelling approach Kamal, Aleena; Kamal, Minahil; Fatima, Mashal; Hussain, Syed Muddusir; Sami Ur Rahman, Jawwad; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp89-100

Abstract

This study explores the intricate relationship between osteoporosis, body mass index (BMI), and the risk of femur fractures using computational modeling. Osteoporosis is a silent metabolic disorder that depletes bone density and structure, significantly increasing the risk of fractures, particularly in weight-bearing bones such as the femur. To analyze the impact of mechanical stress on osteoporotic bones, COMSOL Multiphysics was utilized to simulate stress distribution in a femur under varying BMI conditions, providing valuable insights into how BMI influences bone health and fracture risk. A three-dimensional (3D) femur model was designed using computer-aided design (CAD) software, with specific material properties assigned for both healthy and osteoporotic bones. Finite element analysis was conducted by applying different load conditions, representing body weight, on the femur head. The results highlighted stress distribution and deformation patterns, identifying regions most prone to fracture. The findings demonstrate that while higher BMI typically correlates with increased bone density, it also leads to greater deformation in osteoporotic bones under stress, emphasizing the complex interplay between BMI and bone strength. These insights underscore BMI’s critical role in fracture risk management. Future research should incorporate advanced fracture mechanics models and clinical data to enhance predictive accuracy and develop targeted strategies for fracture prevention in osteoporotic patients.
Holographic-based design, building, and testing of an RRP spherical robot for olive fruits harvesting Al-Habahbeh, Osama M.; Arabiat, Ayeh; Al-Kasasbeh, Riad Taha; Ayoub, Salam
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1602-1612

Abstract

A revolute-revolute-prismatic (RRP) spherical robot has been designed, simulated, built, and tested. The robot is intended to perform olive fruit harvesting tasks. The design simulation is done using hologram tools. The design factors considered include reach, dexterity, accuracy, and productivity. Based on the results of the holographic simulation, a prototype was built and tested on real olive fruits. The end effector is equipped with a rake tool so that the robot can harvest multiple fruits in each stroke. The robot is controlled by Raspberry Pi while a stereovision camera enables 3-D vision. Once the camera detects the fruits, an inverse kinematics algorithm is initiated to find the location of the fruits. The fruit coordinates are commanded to the manipulator to perform the harvesting. The field tests showed that the manipulator is successful in performing the harvesting operations. To increase the harvesting efficiency, it is recommended to build a larger prototype.
Optimizing stress resistance in MEMS inertial sensors through material and thickness variations Aziza, Miladina Rizka; Setyawati, Onny; Jumiadi, Jumiadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp110-117

Abstract

Stress on the micro-electromechnical system (MEMS) sensors significantly decreases sensor accuracy. Thermomechanical stresses induced by the packaging assembly process and external loads during operation induce a shift in the output signal (offset) of MEMS sensors. To achieve high precision in accelerometers, gyroscopes, and other MEMS devices, it is crucial to employ advanced modeling and simulation techniques to mitigate stress-induced offset drift. Therefore, this paper aims to explore and simulate stress on inertial sensors by designing a gyroscope tuning fork with a perforated proof mass to reduce the damping effect. Our findings provide insights for decreasing stress by varying the material and thickness of the inertial sensor. The least stress was obtained from an inertial silicon sensor with 5 and 20 mm thicknesses.
Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD filter-CLAHE Khozaimi, Ach; Darti, Isnani; Anam, Syaiful; Kusumawinahyu, Wuryansari Muharini
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp644-655

Abstract

Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: PeronaMalik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.
CGDE-YOLOv5n: a real-time safety helmet-wearing detection algorithm Luo, Wanbo; Mohd Yassin, Ahmad Ihsan; Mohd Shariff, Khairul Khaizi; Raju, Rajeswari
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1765-1781

Abstract

Due to numerous parameters and calculations, existing safety helmetwearing detection models are challenging to deploy on embedded devices. Therefore, this paper proposed a you only look once (YOLO) v5n-based lightweight detection algorithm called CGDE-YOLOv5n to address the shortcomings in the following areas: (i) the YOLOv5n algorithm was selected to minimize the model’s parameters and calculations, reducing the hardware cost. (ii) The convolutional block attention module (CBAM) was integrated into the backbone to enhance the network’s feature extraction capability. (iii) The neck was improved using the efficient re-parameterized generalized feature pyramid network (efficient RepGFPN) to enhance the multi-scale object detection capability. (iv) The C3 module was improved using the deformable ConvNets v2 (DCNv2) module to enhance the network’s adaptability to geometric changes of objects. (v) The complete intersection over union (CIoU) loss was replaced with focal-efficient IoU (focal-EIoU) loss to reduce the missed detection rate. Experimental results demonstrated that the customized gradient descent estimation (CGDE)- YOLOv5n achieved a mean average precision (mAP) 50 of 89.5% and recall of 84%, which is 1% and 0.8% higher than the YOLOv5n. In particular, the recall of workers not wearing safety helmets increased by 1.7%. Furthermore, the improved model achieved a detection speed of 68.5 frames per second (FPS), meeting the real-time requirements.
High-accuracy classification of banana varieties using ResNet-50 and DenseNet-121 architectures Riska, Suastika Yulia; Sulistyo, Danang Arbian; Siti Maharani, Farah Shafiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp322-335

Abstract

Bananas are a popular fruit in Indonesia due to their affordability, availability, and rich nutritional content. Identifying different banana types is crucial for consumption and processing, yet some types are difficult to distinguish visually. This study aims to classify banana types using convolutional neural network (CNN) architectures, specifically ResNet-50 and DenseNet-121. The dataset consists of five banana classes, which were processed using preprocessing techniques to enhance image quality prior to model training. The results demonstrate that the proposed models can classify banana types with high accuracy. The research methodology includes data collection, preprocessing, CNN model implementation, and performance evaluation using a confusion matrix. The dataset was split into training and testing sets in an 80:20 ratio, with validation data extracted from the training set in a 90:10 ratio. The models were trained on the training data, validated with validation data, and tested on the testing data to assess final performance. The study concludes that the CNN architectures employed are effective in classifying banana types, with the DenseNet-121 model achieving 93.02% accuracy, outperforming the ResNet-50 model, which achieved 92.44%. These results indicate that the models can capture essential features from banana images and produce accurate predictions.
A compact study on methodological insights on navigational systems in vehicular traffic system Thimmappa, Prathibha; Kundu, Mayuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp585-591

Abstract

Navigation system has witnessed a significant inclusion of potential technological advancement in the area of vehicular traffic system. Since the last decade, there are various evolution of innovative techniques that has identified and addressed some serious problem towards vehicular navigation system. With a progress of time, artificial intelligence (AI) has evolved as contributory role model towards optimizing the performance of navigation system. However, still it is quite challenging to acquire a quick snapshot of overall stand of all such methodologies and its effectiveness. Hence, this paper presents a precise, compact, and highly crisp discussion of core taxonomies of methods towards improving navigation system. The paper also contributes towards highlighting their strength and weakness followed by updated research trend to understand the true picture. Finally, the paper contributes to highlight the critical trade-off and gaps.

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